{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torch==2.2 in /opt/conda/lib/python3.10/site-packages (2.2.0)\n",
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      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (1.12)\n",
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      "Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2023.12.2)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.19.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (12.1.105)\n",
      "Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2) (2.2.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2) (12.3.101)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2) (1.3.0)\n",
      "Requirement already satisfied: torchvision==0.17 in /opt/conda/lib/python3.10/site-packages (0.17.0)\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (1.26.2)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.28.1)\n",
      "Requirement already satisfied: torch==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (2.2.0)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision==0.17) (9.3.0)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (4.9.0)\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (1.12)\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.8.5)\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (3.1.2)\n",
      "Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2023.12.2)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.19.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (12.1.105)\n",
      "Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch==2.2.0->torchvision==0.17) (2.2.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.2.0->torchvision==0.17) (12.3.101)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2.1.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (3.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (1.26.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision==0.17) (2022.6.15)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch==2.2.0->torchvision==0.17) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch==2.2.0->torchvision==0.17) (1.3.0)\n",
      "Requirement already satisfied: matplotlib==3.5.2 in /opt/conda/lib/python3.10/site-packages (3.5.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (4.46.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.4.5)\n",
      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (1.26.2)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (21.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (9.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib==3.5.2) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib==3.5.2) (1.16.0)\n",
      "Requirement already satisfied: scikit-image==0.19.3 in /opt/conda/lib/python3.10/site-packages (0.19.3)\n",
      "Requirement already satisfied: numpy>=1.17.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.26.2)\n",
      "Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.11.4)\n",
      "Requirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.8.5)\n",
      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (9.3.0)\n",
      "Requirement already satisfied: imageio>=2.4.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2.33.0)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (2023.12.9)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (1.5.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from scikit-image==0.19.3) (21.3)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->scikit-image==0.19.3) (3.0.9)\n",
      "Requirement already satisfied: opencv-python==4.6.0.66 in /opt/conda/lib/python3.10/site-packages (4.6.0.66)\n",
      "Requirement already satisfied: numpy>=1.21.2 in /opt/conda/lib/python3.10/site-packages (from opencv-python==4.6.0.66) (1.26.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install torch==2.2\n",
    "!pip install torchvision==0.17\n",
    "!pip install matplotlib==3.5.2\n",
    "!pip install scikit-image==0.19.3\n",
    "!pip install opencv-python==4.6.0.66"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(123)\n",
    "torch.use_deterministic_algorithms(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(ConvNet, self).__init__()\n",
    "        self.cn1 = nn.Conv2d(1, 16, 3, 1)\n",
    "        self.cn2 = nn.Conv2d(16, 32, 3, 1)\n",
    "        self.dp1 = nn.Dropout2d(0.10)\n",
    "        self.dp2 = nn.Dropout2d(0.25)\n",
    "        self.fc1 = nn.Linear(4608, 64) # 4608 is basically 12 X 12 X 32\n",
    "        self.fc2 = nn.Linear(64, 10)\n",
    " \n",
    "    def forward(self, x):\n",
    "        x = self.cn1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.cn2(x)\n",
    "        x = F.relu(x)\n",
    "        x = F.max_pool2d(x, 2)\n",
    "        x = self.dp1(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.dp2(x)\n",
    "        x = self.fc2(x)\n",
    "        op = F.log_softmax(x, dim=1)\n",
    "        return op"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define training and inference routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, device, train_dataloader, optim, epoch):\n",
    "    model.train()\n",
    "    for b_i, (X, y) in enumerate(train_dataloader):\n",
    "        X, y = X.to(device), y.to(device)\n",
    "        optim.zero_grad()\n",
    "        pred_prob = model(X)\n",
    "        loss = F.nll_loss(pred_prob, y) # nll is the negative likelihood loss\n",
    "        loss.backward()\n",
    "        optim.step()\n",
    "        if b_i % 10 == 0:\n",
    "            print('epoch: {} [{}/{} ({:.0f}%)]\\t training loss: {:.6f}'.format(\n",
    "                epoch, b_i * len(X), len(train_dataloader.dataset),\n",
    "                100. * b_i / len(train_dataloader), loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, device, test_dataloader):\n",
    "    model.eval()\n",
    "    loss = 0\n",
    "    success = 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in test_dataloader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            pred_prob = model(X)\n",
    "            loss += F.nll_loss(pred_prob, y, reduction='sum').item()  # loss summed across the batch\n",
    "            pred = pred_prob.argmax(dim=1, keepdim=True)  # us argmax to get the most likely prediction\n",
    "            success += pred.eq(y.view_as(pred)).sum().item()\n",
    "\n",
    "    loss /= len(test_dataloader.dataset)\n",
    "\n",
    "    print('\\nTest dataset: Overall Loss: {:.4f}, Overall Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        loss, success, len(test_dataloader.dataset),\n",
    "        100. * success / len(test_dataloader.dataset)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## create data loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The mean and standard deviation values are calculated as the mean of all pixel values of all images in the training dataset\n",
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=True, download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1302,), (0.3069,))])), # train_X.mean()/256. and train_X.std()/256.\n",
    "    batch_size=32, shuffle=True)\n",
    "\n",
    "test_dataloader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=False, \n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1302,), (0.3069,)) \n",
    "                   ])),\n",
    "    batch_size=500, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define optimizer and run training epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cpu\")\n",
    "\n",
    "model = ConvNet()\n",
    "optimizer = optim.Adadelta(model.parameters(), lr=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:1347: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\n",
      "  warnings.warn(warn_msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 [0/60000 (0%)]\t training loss: 2.294348\n",
      "epoch: 1 [320/60000 (1%)]\t training loss: 1.743703\n",
      "epoch: 1 [640/60000 (1%)]\t training loss: 1.272358\n",
      "epoch: 1 [960/60000 (2%)]\t training loss: 1.964490\n",
      "epoch: 1 [1280/60000 (2%)]\t training loss: 0.411745\n",
      "epoch: 1 [1600/60000 (3%)]\t training loss: 0.666613\n",
      "epoch: 1 [1920/60000 (3%)]\t training loss: 0.852746\n",
      "epoch: 1 [2240/60000 (4%)]\t training loss: 0.280735\n",
      "epoch: 1 [2560/60000 (4%)]\t training loss: 0.608666\n",
      "epoch: 1 [2880/60000 (5%)]\t training loss: 0.459875\n",
      "epoch: 1 [3200/60000 (5%)]\t training loss: 0.200465\n",
      "epoch: 1 [3520/60000 (6%)]\t training loss: 0.381452\n",
      "epoch: 1 [3840/60000 (6%)]\t training loss: 0.143269\n",
      "epoch: 1 [4160/60000 (7%)]\t training loss: 0.695926\n",
      "epoch: 1 [4480/60000 (7%)]\t training loss: 0.592977\n",
      "epoch: 1 [4800/60000 (8%)]\t training loss: 0.347031\n",
      "epoch: 1 [5120/60000 (9%)]\t training loss: 0.402775\n",
      "epoch: 1 [5440/60000 (9%)]\t training loss: 0.525813\n",
      "epoch: 1 [5760/60000 (10%)]\t training loss: 0.373715\n",
      "epoch: 1 [6080/60000 (10%)]\t training loss: 0.345820\n",
      "epoch: 1 [6400/60000 (11%)]\t training loss: 0.166799\n",
      "epoch: 1 [6720/60000 (11%)]\t training loss: 0.345126\n",
      "epoch: 1 [7040/60000 (12%)]\t training loss: 0.064074\n",
      "epoch: 1 [7360/60000 (12%)]\t training loss: 0.288761\n",
      "epoch: 1 [7680/60000 (13%)]\t training loss: 0.035006\n",
      "epoch: 1 [8000/60000 (13%)]\t training loss: 0.131319\n",
      "epoch: 1 [8320/60000 (14%)]\t training loss: 0.114256\n",
      "epoch: 1 [8640/60000 (14%)]\t training loss: 0.177033\n",
      "epoch: 1 [8960/60000 (15%)]\t training loss: 0.037013\n",
      "epoch: 1 [9280/60000 (15%)]\t training loss: 0.055339\n",
      "epoch: 1 [9600/60000 (16%)]\t training loss: 0.079561\n",
      "epoch: 1 [9920/60000 (17%)]\t training loss: 0.163464\n",
      "epoch: 1 [10240/60000 (17%)]\t training loss: 0.076177\n",
      "epoch: 1 [10560/60000 (18%)]\t training loss: 0.183408\n",
      "epoch: 1 [10880/60000 (18%)]\t training loss: 0.090588\n",
      "epoch: 1 [11200/60000 (19%)]\t training loss: 0.260131\n",
      "epoch: 1 [11520/60000 (19%)]\t training loss: 0.043986\n",
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      "epoch: 1 [59840/60000 (100%)]\t training loss: 0.014311\n",
      "\n",
      "Test dataset: Overall Loss: 0.0475, Overall Accuracy: 9844/10000 (98%)\n",
      "\n",
      "epoch: 2 [0/60000 (0%)]\t training loss: 0.119430\n",
      "epoch: 2 [320/60000 (1%)]\t training loss: 0.008598\n",
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      "epoch: 2 [58240/60000 (97%)]\t training loss: 0.016976\n",
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      "epoch: 2 [59520/60000 (99%)]\t training loss: 0.000814\n",
      "epoch: 2 [59840/60000 (100%)]\t training loss: 0.038551\n",
      "\n",
      "Test dataset: Overall Loss: 0.0422, Overall Accuracy: 9856/10000 (99%)\n",
      "\n",
      "epoch: 3 [0/60000 (0%)]\t training loss: 0.015018\n",
      "epoch: 3 [320/60000 (1%)]\t training loss: 0.010164\n",
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      "epoch: 3 [55680/60000 (93%)]\t training loss: 0.018315\n",
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      "epoch: 3 [56320/60000 (94%)]\t training loss: 0.040984\n",
      "epoch: 3 [56640/60000 (94%)]\t training loss: 0.000689\n",
      "epoch: 3 [56960/60000 (95%)]\t training loss: 0.007462\n",
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      "epoch: 3 [57600/60000 (96%)]\t training loss: 0.160275\n",
      "epoch: 3 [57920/60000 (97%)]\t training loss: 0.061971\n",
      "epoch: 3 [58240/60000 (97%)]\t training loss: 0.002083\n",
      "epoch: 3 [58560/60000 (98%)]\t training loss: 0.003416\n",
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      "epoch: 3 [59520/60000 (99%)]\t training loss: 0.015730\n",
      "epoch: 3 [59840/60000 (100%)]\t training loss: 0.040238\n",
      "\n",
      "Test dataset: Overall Loss: 0.0344, Overall Accuracy: 9886/10000 (99%)\n",
      "\n",
      "epoch: 4 [0/60000 (0%)]\t training loss: 0.110118\n",
      "epoch: 4 [320/60000 (1%)]\t training loss: 0.041794\n",
      "epoch: 4 [640/60000 (1%)]\t training loss: 0.016891\n",
      "epoch: 4 [960/60000 (2%)]\t training loss: 0.050877\n",
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      "epoch: 4 [1600/60000 (3%)]\t training loss: 0.004399\n",
      "epoch: 4 [1920/60000 (3%)]\t training loss: 0.009932\n",
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      "epoch: 4 [59840/60000 (100%)]\t training loss: 0.017924\n",
      "\n",
      "Test dataset: Overall Loss: 0.0356, Overall Accuracy: 9885/10000 (99%)\n",
      "\n",
      "epoch: 5 [0/60000 (0%)]\t training loss: 0.035247\n",
      "epoch: 5 [320/60000 (1%)]\t training loss: 0.051318\n",
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      "epoch: 5 [59840/60000 (100%)]\t training loss: 0.002643\n",
      "\n",
      "Test dataset: Overall Loss: 0.0318, Overall Accuracy: 9900/10000 (99%)\n",
      "\n",
      "epoch: 6 [0/60000 (0%)]\t training loss: 0.005471\n",
      "epoch: 6 [320/60000 (1%)]\t training loss: 0.005731\n",
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      "epoch: 6 [57920/60000 (97%)]\t training loss: 0.095429\n",
      "epoch: 6 [58240/60000 (97%)]\t training loss: 0.001795\n",
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      "epoch: 6 [59840/60000 (100%)]\t training loss: 0.000539\n",
      "\n",
      "Test dataset: Overall Loss: 0.0324, Overall Accuracy: 9906/10000 (99%)\n",
      "\n",
      "epoch: 7 [0/60000 (0%)]\t training loss: 0.023058\n",
      "epoch: 7 [320/60000 (1%)]\t training loss: 0.025277\n",
      "epoch: 7 [640/60000 (1%)]\t training loss: 0.068229\n",
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      "epoch: 7 [59840/60000 (100%)]\t training loss: 0.010451\n",
      "\n",
      "Test dataset: Overall Loss: 0.0346, Overall Accuracy: 9897/10000 (99%)\n",
      "\n",
      "epoch: 8 [0/60000 (0%)]\t training loss: 0.010967\n",
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      "epoch: 8 [59840/60000 (100%)]\t training loss: 0.037734\n",
      "\n",
      "Test dataset: Overall Loss: 0.0345, Overall Accuracy: 9901/10000 (99%)\n",
      "\n",
      "epoch: 9 [0/60000 (0%)]\t training loss: 0.249858\n",
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      "epoch: 9 [59840/60000 (100%)]\t training loss: 0.001231\n",
      "\n",
      "Test dataset: Overall Loss: 0.0366, Overall Accuracy: 9907/10000 (99%)\n",
      "\n",
      "epoch: 10 [0/60000 (0%)]\t training loss: 0.026886\n",
      "epoch: 10 [320/60000 (1%)]\t training loss: 0.069728\n",
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      "epoch: 10 [59840/60000 (100%)]\t training loss: 0.093881\n",
      "\n",
      "Test dataset: Overall Loss: 0.0304, Overall Accuracy: 9916/10000 (99%)\n",
      "\n",
      "epoch: 11 [0/60000 (0%)]\t training loss: 0.000799\n",
      "epoch: 11 [320/60000 (1%)]\t training loss: 0.008168\n",
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      "epoch: 11 [59840/60000 (100%)]\t training loss: 0.001032\n",
      "\n",
      "Test dataset: Overall Loss: 0.0316, Overall Accuracy: 9906/10000 (99%)\n",
      "\n",
      "epoch: 12 [0/60000 (0%)]\t training loss: 0.028874\n",
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      "epoch: 12 [59840/60000 (100%)]\t training loss: 0.029256\n",
      "\n",
      "Test dataset: Overall Loss: 0.0296, Overall Accuracy: 9916/10000 (99%)\n",
      "\n",
      "epoch: 13 [0/60000 (0%)]\t training loss: 0.019384\n",
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      "epoch: 13 [59840/60000 (100%)]\t training loss: 0.001168\n",
      "\n",
      "Test dataset: Overall Loss: 0.0415, Overall Accuracy: 9906/10000 (99%)\n",
      "\n",
      "epoch: 14 [0/60000 (0%)]\t training loss: 0.001558\n",
      "epoch: 14 [320/60000 (1%)]\t training loss: 0.007151\n",
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      "\n",
      "Test dataset: Overall Loss: 0.0320, Overall Accuracy: 9914/10000 (99%)\n",
      "\n",
      "epoch: 15 [0/60000 (0%)]\t training loss: 0.004177\n",
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      "epoch: 15 [59840/60000 (100%)]\t training loss: 0.150484\n",
      "\n",
      "Test dataset: Overall Loss: 0.0346, Overall Accuracy: 9909/10000 (99%)\n",
      "\n",
      "epoch: 16 [0/60000 (0%)]\t training loss: 0.001200\n",
      "epoch: 16 [320/60000 (1%)]\t training loss: 0.001011\n",
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      "epoch: 16 [59840/60000 (100%)]\t training loss: 0.029706\n",
      "\n",
      "Test dataset: Overall Loss: 0.0346, Overall Accuracy: 9901/10000 (99%)\n",
      "\n",
      "epoch: 17 [0/60000 (0%)]\t training loss: 0.018773\n",
      "epoch: 17 [320/60000 (1%)]\t training loss: 0.007805\n",
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      "epoch: 17 [59840/60000 (100%)]\t training loss: 0.002149\n",
      "\n",
      "Test dataset: Overall Loss: 0.0362, Overall Accuracy: 9908/10000 (99%)\n",
      "\n",
      "epoch: 18 [0/60000 (0%)]\t training loss: 0.001782\n",
      "epoch: 18 [320/60000 (1%)]\t training loss: 0.037850\n",
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      "epoch: 18 [51520/60000 (86%)]\t training loss: 0.000689\n",
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      "epoch: 18 [52160/60000 (87%)]\t training loss: 0.000245\n",
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      "epoch: 18 [52800/60000 (88%)]\t training loss: 0.000945\n",
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      "epoch: 18 [53440/60000 (89%)]\t training loss: 0.160516\n",
      "epoch: 18 [53760/60000 (90%)]\t training loss: 0.021920\n",
      "epoch: 18 [54080/60000 (90%)]\t training loss: 0.001036\n",
      "epoch: 18 [54400/60000 (91%)]\t training loss: 0.239800\n",
      "epoch: 18 [54720/60000 (91%)]\t training loss: 0.000424\n",
      "epoch: 18 [55040/60000 (92%)]\t training loss: 0.009514\n",
      "epoch: 18 [55360/60000 (92%)]\t training loss: 0.016584\n",
      "epoch: 18 [55680/60000 (93%)]\t training loss: 0.000663\n",
      "epoch: 18 [56000/60000 (93%)]\t training loss: 0.000081\n",
      "epoch: 18 [56320/60000 (94%)]\t training loss: 0.000378\n",
      "epoch: 18 [56640/60000 (94%)]\t training loss: 0.000050\n",
      "epoch: 18 [56960/60000 (95%)]\t training loss: 0.000158\n",
      "epoch: 18 [57280/60000 (95%)]\t training loss: 0.002465\n",
      "epoch: 18 [57600/60000 (96%)]\t training loss: 0.004555\n",
      "epoch: 18 [57920/60000 (97%)]\t training loss: 0.001574\n",
      "epoch: 18 [58240/60000 (97%)]\t training loss: 0.001144\n",
      "epoch: 18 [58560/60000 (98%)]\t training loss: 0.005433\n",
      "epoch: 18 [58880/60000 (98%)]\t training loss: 0.000281\n",
      "epoch: 18 [59200/60000 (99%)]\t training loss: 0.008778\n",
      "epoch: 18 [59520/60000 (99%)]\t training loss: 0.054680\n",
      "epoch: 18 [59840/60000 (100%)]\t training loss: 0.000720\n",
      "\n",
      "Test dataset: Overall Loss: 0.0348, Overall Accuracy: 9914/10000 (99%)\n",
      "\n",
      "epoch: 19 [0/60000 (0%)]\t training loss: 0.000451\n",
      "epoch: 19 [320/60000 (1%)]\t training loss: 0.107797\n",
      "epoch: 19 [640/60000 (1%)]\t training loss: 0.000284\n",
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      "epoch: 19 [51520/60000 (86%)]\t training loss: 0.000428\n",
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      "epoch: 19 [52160/60000 (87%)]\t training loss: 0.000038\n",
      "epoch: 19 [52480/60000 (87%)]\t training loss: 0.078580\n",
      "epoch: 19 [52800/60000 (88%)]\t training loss: 0.005430\n",
      "epoch: 19 [53120/60000 (89%)]\t training loss: 0.001048\n",
      "epoch: 19 [53440/60000 (89%)]\t training loss: 0.034718\n",
      "epoch: 19 [53760/60000 (90%)]\t training loss: 0.001970\n",
      "epoch: 19 [54080/60000 (90%)]\t training loss: 0.000135\n",
      "epoch: 19 [54400/60000 (91%)]\t training loss: 0.002385\n",
      "epoch: 19 [54720/60000 (91%)]\t training loss: 0.111812\n",
      "epoch: 19 [55040/60000 (92%)]\t training loss: 0.000911\n",
      "epoch: 19 [55360/60000 (92%)]\t training loss: 0.001384\n",
      "epoch: 19 [55680/60000 (93%)]\t training loss: 0.000391\n",
      "epoch: 19 [56000/60000 (93%)]\t training loss: 0.000602\n",
      "epoch: 19 [56320/60000 (94%)]\t training loss: 0.000179\n",
      "epoch: 19 [56640/60000 (94%)]\t training loss: 0.000022\n",
      "epoch: 19 [56960/60000 (95%)]\t training loss: 0.002838\n",
      "epoch: 19 [57280/60000 (95%)]\t training loss: 0.001844\n",
      "epoch: 19 [57600/60000 (96%)]\t training loss: 0.147678\n",
      "epoch: 19 [57920/60000 (97%)]\t training loss: 0.000999\n",
      "epoch: 19 [58240/60000 (97%)]\t training loss: 0.037533\n",
      "epoch: 19 [58560/60000 (98%)]\t training loss: 0.001186\n",
      "epoch: 19 [58880/60000 (98%)]\t training loss: 0.012587\n",
      "epoch: 19 [59200/60000 (99%)]\t training loss: 0.000077\n",
      "epoch: 19 [59520/60000 (99%)]\t training loss: 0.000058\n",
      "epoch: 19 [59840/60000 (100%)]\t training loss: 0.002629\n",
      "\n",
      "Test dataset: Overall Loss: 0.0418, Overall Accuracy: 9918/10000 (99%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, 20):\n",
    "    train(model, device, train_dataloader, optimizer, epoch)\n",
    "    test(model, device, test_dataloader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## run inference on trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_samples = enumerate(test_dataloader)\n",
    "b_i, (sample_data, sample_targets) = next(test_samples)\n",
    "\n",
    "plt.imshow(sample_data[0][0], cmap='gray', interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model prediction is : 4\n",
      "Ground truth is : 4\n"
     ]
    }
   ],
   "source": [
    "print(f\"Model prediction is : {model(sample_data).data.max(1)[1][0]}\")\n",
    "print(f\"Ground truth is : {sample_targets[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualize filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1)),\n",
       " Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1)),\n",
       " Dropout2d(p=0.1, inplace=False),\n",
       " Dropout2d(p=0.25, inplace=False),\n",
       " Linear(in_features=4608, out_features=64, bias=True),\n",
       " Linear(in_features=64, out_features=10, bias=True)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_children_list = list(model.children())\n",
    "convolutional_layers = []\n",
    "model_parameters = []\n",
    "model_children_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(model_children_list)):\n",
    "    if type(model_children_list[i]) == nn.Conv2d:\n",
    "        model_parameters.append(model_children_list[i].weight)\n",
    "        convolutional_layers.append(model_children_list[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "for i, flt in enumerate(model_parameters[0]):\n",
    "    plt.subplot(4, 4, i+1)\n",
    "    plt.imshow(flt[0, :, :].detach(), cmap='gray')\n",
    "    plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x800 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 8))\n",
    "for i, flt in enumerate(model_parameters[1]):\n",
    "    plt.subplot(8, 4, i+1)\n",
    "    plt.imshow(flt[0, :, :].detach(), cmap='gray')\n",
    "    plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualize feature maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_layer_results = [convolutional_layers[0](sample_data)]\n",
    "for i in range(1, len(convolutional_layers)):\n",
    "    per_layer_results.append(convolutional_layers[i](per_layer_results[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 26, 26])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "layer_visualisation = per_layer_results[0][0, :, :, :]\n",
    "layer_visualisation = layer_visualisation.data\n",
    "print(layer_visualisation.size())\n",
    "for i, flt in enumerate(layer_visualisation):\n",
    "    plt.subplot(4, 4, i + 1)\n",
    "    plt.imshow(flt, cmap='gray')\n",
    "    plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 24, 24])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x800 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 8))\n",
    "layer_visualisation = per_layer_results[1][0, :, :, :]\n",
    "layer_visualisation = layer_visualisation.data\n",
    "print(layer_visualisation.size())\n",
    "for i, flt in enumerate(layer_visualisation):\n",
    "    plt.subplot(8, 4, i + 1)\n",
    "    plt.imshow(flt, cmap='gray')\n",
    "    plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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